Hippocampal asymmetry measure using Magnetic Resonance Imaging is a sensitive index of the lateralization of temporal lobe epilepsy. When the focus is confined to one hemisphere, surgery can provide considerable relief. Thus, measurement of hippocampal volume can provide information crucial for surgical planning. However, segmentation of the hippocampus remains non-trivial and is presently performed by laborious inefficient and difficult to reproduce manual procedures requiring expertly trained users. To date, no successful method is available to automate this procedure due to the poorly defined hippocampal boundary on MR images. Thus, a new approach is required in order to increase efficiency and predictive value of MRI for surgical planning gin the treatment of epilepsy. In this proposal, a potentially novel shape recovery method using a multi-resolution wavelet basis framework incorporating prior information on the hippocampus will be developed . The method will be trained using 90 retrospective clinical exams performed by an expertly trained neuroscientist. Preliminary data already demonstrate the great potential of this methodology. A user friendly Visualization and Menstruation Program (VAMP) will be developed as a clinical interface. When developed, VAMP will be tested retrospectively on 120 patient data sets not user for training the algorithm and the correlation between automatically determined hippocampal volumes and seizure frequency determined.